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Gramian Consulting Group

Taxonomy & Ontology Curator

Posted 5 Hours Ago
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Remote
Hiring Remotely in United Kingdom
Mid level
Remote
Hiring Remotely in United Kingdom
Mid level
Curate and grow the Ads Knowledge Graph by onboarding entities, validating and correcting LLM-generated entries, performing data labeling and QA, tuning prompts, creating golden sets, resolving data integrity and duplicate issues, and coordinating with Product, Engineering, and Analytics stakeholders.
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About Us

Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs.

Role overview

Our client is a large-scale, high-traffic social content platform whose Ads Content Understanding team builds the core engine for accurate, interpretable understanding of organic and paid content. Using applied ML and a robust Knowledge Graph (KG), the team extracts high-quality, monetization-focused signals that power ads performance and business insights at scale.

We are seeking a Knowledge Graph Curator / Expert to grow and curate Knowledge Graph of entities and relationships. The role sits at the intersection of AI automation and human judgment, managing partner team requests while proactively shaping the KG to ensure high fidelity, relevance, and connectivity. As the human-in-the-loop, you will validate LLM-generated entities and ensure the graph remains the source of ground truth for the business.

Locations: 100% Remote - Armenia, Bulgaria, Croatia, Egypt, Hungary, Poland, Romania, Slovenia, UAE, UK, Ireland, Netherlands
Contract: 12-18 months contract or FTE

Note: This is not a job for an engineer (Data Scientist, AI/ML). This is a Data Analyst/Entry type of a role.

About the Team

The Indexing Team is part of the ML organization, specializing in indexing: processing massive datasets to enable ranking, search, and more. We handle real-time event processing and asynchronous batch jobs from diverse sources.

We partner closely with backend and ML teams to integrate our Indexing Platform with ML services, ensuring frictionless index creation and top-tier customer experiences.

Our platform fuels high-impact features: enterprise search, user feed ranking, and content understanding— all at B2C scale (10M+ DAU).

Key Responsibilities
  • Onboard new entities into the Ads team Knowledge Graph, ensuring alignment with defined schemas and ontologies
  • Serve as the primary point of contact for Product, Engineering, and Analytics teams requesting entity additions or schema changes
  • Prioritize and triage incoming requests based on business impact, urgency, and technical feasibility
  • Perform data entry and labeling to support automated content understanding pipelines
  • Tune and refine LLM prompts to improve content understanding automation
  • Review and curate LLM-generated entities, approving high-confidence outputs, correcting near-misses, and rejecting hallucinations
  • Execute rigorous QA and validation to ensure ontological consistency and factual accuracy
  • Create Golden Sets via targeted labeling to assess model quality and support classifier and extraction model fine-tuning
  • Investigate and resolve data integrity issues, including error correction, duplicate entity merging, and relationship conflict resolution
  • Provide clear stakeholder feedback, status updates, and modeling rationale, and guide teams on effective Knowledge Graph querying

Requirements
  • 3+ years of experience with Knowledge Graph
  • Strong Understanding of graph concepts (Nodes, Edges, Properties)
  • Experience with Taxonomy & Ontology - Experience categorizing data, managing hierarchies, and understanding semantic relationships between entities.
  • Proficiency in navigating complex datasets.
  • Experience with SQL, SPARQL, or Cypher is a strong plus.
  • Understanding of how Generative AI works, common failure modes (hallucinations), and the importance of ground-truth data in training.

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